⚡ Bolt: Optimize file upload memory usage

.gitignore

@@ -1,3 +1,4 @@
 .gitignore
 /venv
-/.pytest_cache
+/.pytest_cache
+__pycache__/

.jules/bolt.md

@@ -1,3 +1,7 @@
 ## 2024-05-23 - [Regex Pre-compilation in Loops]
 **Learning:** Pre-compiling regular expressions (`re.compile`) at the module level provides a significant performance boost (measured ~1.8x speedup) when the regex is used inside a tight loop or a pandas `apply` function, compared to compiling it repeatedly or implicitly inside the loop. Vectorized string operations in Pandas are usually faster, but in complex logic cases (multiple prioritized regex groups + fallback logic), a simple pre-compiled regex with `apply` can sometimes be cleaner and sufficiently fast, or even faster if the vectorized approach requires multiple passes or expensive intermediate structures.
 **Action:** Always check for regex usage in loops or `apply` calls. If found, refactor to use module-level pre-compiled patterns. When considering vectorization, benchmark against the optimized loop version, as the overhead of complex vectorization might outweigh the benefits for moderate dataset sizes.
+
+## 2025-05-23 - [Streamlit File Upload Memory Optimization]
+**Learning:** When handling file uploads in Streamlit (or other web frameworks), reading the entire file into memory with `.read().decode()` creates a massive memory spike (2x-3x file size) due to holding raw bytes, decoded string, and the file buffer simultaneously. For text-based formats (like MGF/mzTab), wrapping the binary stream directly with `io.TextIOWrapper` allows for streaming processing, significantly reducing memory usage without sacrificing functionality.
+**Action:** Use `io.TextIOWrapper` for processing text-based file uploads instead of reading/decoding fully into memory.

app.py

@@ -32,9 +32,10 @@ def run_streamlit_app():
     # Process files only when both are uploaded
     if mgf_file and mztab_file:
         # Decode uploaded file contents (Streamlit files are bytes by default)
-        # Use StringIO to create file-like objects for pyteomics parsers
-        spectra = load_mgf(io.StringIO(mgf_file.read().decode('utf-8')))
-        psm_df = load_mztab(io.StringIO(mztab_file.read().decode('utf-8')))
+        # ⚡ OPTIMIZATION: Use TextIOWrapper to stream decoded text instead of reading full file into memory
+        # This significantly reduces memory usage for large MGF/mzTab files
+        spectra = load_mgf(io.TextIOWrapper(mgf_file, encoding='utf-8'))
+        psm_df = load_mztab(io.TextIOWrapper(mztab_file, encoding='utf-8'))
 
         # Create mappings between PSMs and spectra
         mapped = map_psms_to_spectra(spectra, psm_df)